IJIRST (International Journal for Innovative Research in Science & Technology)ISSN (online) : 2349-6010

 International Journal for Innovative Research in Science & Technology

Solution of Unit Commitment by Artificial Intelligence Techniques


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International Journal for Innovative Research in Science & Technology
Volume 5 Issue - 6
Year of Publication : 2018
Authors : Ashutosh Kantilal Parmar ; Dipesh Doshi

BibTeX:

@article{IJIRSTV5I6012,
     title={Solution of Unit Commitment by Artificial Intelligence Techniques},
     author={Ashutosh Kantilal Parmar and Dipesh Doshi},
     journal={International Journal for Innovative Research in Science & Technology},
     volume={5},
     number={6},
     pages={38--44},
     year={},
     url={http://www.ijirst.org/articles/IJIRSTV5I6012.pdf},
     publisher={IJIRST (International Journal for Innovative Research in Science & Technology)},
}



Abstract:

An important criterion in power system is to meet the power demand at minimum fuel cost using an optimal mix of different power plants. Moreover, in order to supply electric power to customers in a secured and economic manner, unit commitment is considered to be one of the best available options. It is thus recognize that the optimal unit commitment results in a great saving for electric utilities. Unit Commitment is the problem of determining the schedule of generating units subject to device and operating constraints. The unit commitment has been identified for the thesis work. The formulation of unit commitment has been discussed and the solution is obtained by classic Dynamic Programming method, Ant Colony Optimization technique and or by Particle Swarm Optimization method. MATLAB codes have been generated for all the three methods to solve the unit commitment problem. The effectiveness of these methods has been tested on two systems comprising three units and six units and total operating cost is obtained. The results of unit commitment problem by all the three methods are compared for total operating cost and for computation time.


Keywords:

Unit Commitment Problem, Artificial Intelligence Techniques, Comparison with Conventional Techniques


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